The present invention relates to the field of revenue management. In particular, the present invention can access available flight segment-level unconstrained passenger demand forecasts for all scheduled flight segments in an airline""s network and historical origin-destination level passenger demands for all origin-destination pairs served by the airline and compute unconstrained passenger demand forecasts for all available service products in the airline""s network flight schedule. This allows the airline to have better data in maximizing revenues from the sale of its inventory of service products.
Growth in the transportation business and, in particular, the airline industry has resulted in the increased use of central reservation host computers for providing schedule, flight, fare, and availability information on a real-time request basis. Historically, the host computer""s response to consumer requests for service products was supported by accessing their value category and the corresponding flight segment availability stored on a central reservation database. While prior systems, such as the one just described, have provided airline revenue management on the flight-segment level, they proved to be very inefficient, as passengers typically request service products by origin and destination rather than by flight segment, and airlines typically price services by origin and destination as well.
An Origin-Destination Revenue Management System (ODRMS) provides improved revenue management capability for airlines by leveraging the value derived from origin-destination (OD) information of passengers. It allows revenue management control to be better aligned with the way passengers plan their travel and the way airlines price their service products.
To understand the distinction between flight segments, OD pairs and service products joining an OD pair, an example may be appropriate. A passenger requests travel service from Atlanta, Ga. (ATL) to Los Angeles, Calif. (LAX). Depending on the availability, an airline might offer various service products to satisfy the request: (1) a non-stop ATL-LAX flight, or (2) an itinerary with a stop in Dallas, Tex. (DFW) consisting of the flight segments ATL-DFW and DFW-LAX. While ATL-LAX is the OD pair in either service product options, the service product in option (1) consists of one flight segment, while the service product in option (2) consists of two flight segments.
Central to a typical ODRMS is a component that produces minimum acceptable fares for each service product an airline sells by optimizing the total revenue for an airline""s network flight schedule. This component takes as its input the airline""s network flight schedule, service product fares and unconstrained passenger demand forecast for all available service products. The term unconstrained demand forecast refers to a demand forecast inferred from both the demands that are observed (e.g., flown passengers) and demands are not observed (e.g., passengers not accommodated due to limited capacity). Apart from the service product unconstrained passenger demand forecasts, the airline has almost full control over the rest of the input components as these are predetermined by the airline. It turns out, however, that forecasting passenger demands for an airline""s service products is not a trivial task.
Currently, most airlines have network revenue management systems in place, which forecast unconstrained passenger demand at the segment level. As discussed earlier, however, to determine the minimum acceptable fares for origin-destination service products in an ODRMS, service product level forecasts at the origin-destination level are required. The ability to infer service product level unconstrained demand forecasts from segment level unconstrained demand forecast is attractive as it allows for the enhancement of currently active systems for use in an ODRMS with minimal cost and maximum utilization of existing systems.
Prior ODRMS attempted to infer service product level unconstrained demand from segment level unconstrained demand using a technique known as parsing. In the parsing method, the system uses historical data to determine the percentages representing the proportion of the segment level demand that is attributable to a particular OD service product using a segment. From the percentages, service product level unconstrained demand forecast might be derived. In some cases, conflicting service product level unconstrained demand forecasts might be derived by parsing different segments used by the same service product. Methods used to reconcile the conflicting demand forecasts include selecting the minimum demand forecast, selecting the maximum demand forecast, taking a weighted mean of the variant forecasts, or taking the median of the variant forecasts. In practice, none of these reconciliation methods are satisfactory as the service product unconstrained demand forecasts become inconsistent with the segment level unconstrained demand forecasts and thus, less accurate. Prior systems also fail to take into account consumer preference for available service products joining an OD pair in determining service product level unconstrained demand forecasts.
In view of the foregoing, there is a need for an improved origin-destination service product unconstrained demand forecast inference system in the revenue management field.
An airline origin-destination (OD) revenue management software system supports decisions to accept or deny requests for booking airline seats by comparing the fare for the request with a minimum acceptable fare predetermined by the system. In order to determine the minimum acceptable fare, the system typically solves an optimization problem that accepts as inputs the airline""s network flight schedule, service product fares, unconstrained passenger demand forecast for all service products available for booking on the airline""s flight network, and available capacity on each of the airline""s scheduled flight segments.
The inventive methods and system disclosed herein provide a means for accessing a centrally located information repository and retrieving inventory resource type and value information in an execution environment that allows a determination of an estimated origin-destination service product unconstrained demand for a given origin, destination and service product. Thus, one aspect of the present invention is to provide an execution environment that best estimates unconstrained demand for all service products between an origin and a destination based on segment level forecasts, historical demand, available service products, current flight schedule, and historical consumer preference.
The present invention supports a calculation of an estimated origin-destination service product unconstrained demand. Estimated origin-destination service product unconstrained demand represents consumer demand for an origin-destination service product. Furthermore, the demand is denoted as an unconstrained demand because it typically includes the number of consumers who will book a flight from the origin city to the destination city and those consumers who might be denied an opportunity to book a flight or chose not to book a flight.
The calculation begins by retrieving a segment level unconstrained demand forecast for each segment within an origin-destination pair. The segment level forecast can be retrieved from one or more forecasting systems connected to a computer network and represents consumer demand for a segment. The description of the difference between a flight segment, an OD pair and service products joining an OD pair can best be understood from a representative example for the air transportation field. A passenger requests travel service from Atlanta (ATL) to Los Angeles (LAX). Depending on the availability, an airline might offer various service products to satisfy the request: (1) a non-stop ATL-LAX flight, or (2) an itinerary with a stop in Dallas (DFW) consisting of the flight segments ATL-DFW and DFW-LAX. While ATL-LAX is the OD pair in either service product options, the service product in option (1) consists of one flight segment, while the service product in option (2) consists of two flight segments.
A consumer preference for service products joining an origin-destination pair can be generated by a consumer product preference analyzer, connected to the computer network. The analyzer can be implemented by a product preference analysis computer. The preference typically represents the probability that a consumer traveling on a particular origin-destination pair will use a particular service product, in the form of a matrix. The preference can be generated using historical passenger data stored on a set of information databases connected to the computer network. The information databases typically represent a passenger name record database comprising departure dates, flight origin, flight destination, departure time, class of service, flight segments, amount a consumer paid for the service product, and historical origin-destination pair demand. A network flight schedule can then be determined within the analyzer. The network flight schedule typically represents a determination whether a service product uses a particular flight segment. The schedule can be generated by analyzing and comparing information gleaned from consumer preference for service origin-destination service products and the scheduled flight information derived from the description of the segment level unconstrained demand forecast.
A historical origin-destination pair demand can then be retrieved from a set of information databases connected to the computer network. These databases typically represent the reservation database and the passenger name record database. Historical origin-destination pair demands can be denoted as a vector representing demand levels for all origin-destination pairs serviced by the airline. A scaled historical origin-destination passenger demand can be determined based on the historical origin-destination demand and a determination of whether the destination is an important destination for the origin. Determination of importance is typically affected by a comparison of the historical demand from an origin to a destination as compared to the total historical demand of all products originating from the origin city. A scaled historical origin-destination passenger demand can be used in place of historical origin-destination passenger demand in the continuing method.
An origin-destination unconstrained demand can be determined and is typically represented by an estimated origin-destination pair unconstrained demand. Estimated origin-destination pair unconstrained demand can be generated by a demand forecasting determiner connected to a computer network. The demand forecasting determiner is typically implemented by an origin-destination forecast inference computer. Estimated origin-destination unconstrained demand represents the future demand level for an origin-destination pair, without reference to the service product chosen by the consumer. Estimated origin-destination unconstrained demand is typically determined by solving a least squares optimization problem represented as a quadratic program. The quadratic program can accept as its inputs: segment level unconstrained demand forecast, consumer preference for origin-destination service products, historical origin-destination pair demand, and the network flight schedule. The quadratic program can also accept the additional input of a historical demand adjustment factor. The historical demand adjustment factor is typically input by the inventory manager from a user input terminal and can be used to adjust the relative importance of the historical origin-destination observed demands against future segment unconstrained demand estimations.
An estimated origin-destination service product unconstrained demand can be generated by the origin-destination forecast inference computer. The inference computer can use the estimated origin-destination unconstrained demand and the consumer preference for products within an origin-destination pair to generate estimated origin-destination service product unconstrained demand.